VAICo: Visual Analysis for Image Comparison

Johanna Schmidt, M. Eduard Gröller, Stefan Bruckner

Abstract

Scientists, engineers, and analysts are confronted with ever larger
and more complex sets of data, whose analysis poses special challenges.
In many situations it is necessary to compare two or more datasets.
Hence there is a need for comparative visualization tools to help
analyze differences or similarities among datasets. In this paper
an approach for comparative visualization for sets of images is presented.
Well-established techniques for comparing images frequently place
them side-by-side. A major drawback of such approaches is that they
do not scale well. Other image comparison methods encode differences
in images by abstract parameters like color. In this case information
about the underlying image data gets lost. This paper introduces
a new method for visualizing differences and similarities in large
sets of images which preserves contextual information, but also allows
the detailed analysis of subtle variations. Our approach identifies
local changes and applies cluster analysis techniques to embed them
in a hierarchy. The results of this process are then presented in
an interactive web application which allows users to rapidly explore
the space of differences and drill-down on particular features. We
demonstrate the flexibility of our approach by applying it to multiple
distinct domains.

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BibTeX

@ARTICLE{Schmidt-2013-VVA,
author = {Johanna Schmidt and M. Eduard Gr{\"o}ller and Stefan Bruckner},
title = {VAICo: Visual Analysis for Image Comparison},
journal = {IEEE Transactions on Visualization and Computer Graphics},
year = {2013},
volume = {19},
pages = {2090--2099},
number = {12},
month = dec,
abstract = {Scientists, engineers, and analysts are confronted with ever larger
and more complex sets of data, whose analysis poses special challenges.
In many situations it is necessary to compare two or more datasets.
Hence there is a need for comparative visualization tools to help
analyze differences or similarities among datasets. In this paper
an approach for comparative visualization for sets of images is presented.
Well-established techniques for comparing images frequently place
them side-by-side. A major drawback of such approaches is that they
do not scale well. Other image comparison methods encode differences
in images by abstract parameters like color. In this case information
about the underlying image data gets lost. This paper introduces
a new method for visualizing differences and similarities in large
sets of images which preserves contextual information, but also allows
the detailed analysis of subtle variations. Our approach identifies
local changes and applies cluster analysis techniques to embed them
in a hierarchy. The results of this process are then presented in
an interactive web application which allows users to rapidly explore
the space of differences and drill-down on particular features. We
demonstrate the flexibility of our approach by applying it to multiple
distinct domains.},
doi = {10.1109/TVCG.2013.213},
event = {IEEE VIS 2013},
keywords = {focus+context visualization, image set comparison, comparative visualization},
url = {http://www.cg.tuwien.ac.at/research/publications/2013/schmidt-2013-vaico/}
}